Disparate, non-standard, real-world data is collected from varied source systems (e.g., payer, provider, consumer, SDOH), formats, and transports (e.g., HL7, CCDA, FHIR, CCLF, EHR).
Data is aggregated according to unique individuals, providers, and clinical concepts. Identity privacy-preserving record linking ensures records belonging to the same individual from different sources are consistently linked together.
Data is stored in a consistent patient-centric model, regardless of source or data type. Source data provenance is maintained in its original form for traceability.
Data is harmonized and standardized automatically, ensuring dirty, uncoded concepts are mapped to standard codesystems (e.g., ICD-10, RxNORM, SNOMED). Once coded, concepts are mapped across multiple terminology standards (e.g., ICD-10 to SNOMED, NDC to RxNORM).
Similar codes are grouped together (e.g., therapeutic classes, drug ingredients, HCC categories, AHRQ value sets) to make data analytically ready.
Enrich & Transform
Unstructured data is stacked, normalized, and transformed into APIs and data pipelines (e.g., Tableau, SQL, Jupyter, Amazon Redshift, Snowflake, FHIR, OMOP) to feed varied applications and use cases.
No data left behind
Raw data from various sources is loaded via Orchestrate and aggregated according to unique individuals, providers, and clinical concepts.
Creating the lifetime person record
Data is cleaned and standardized automatically, ensuring patient information is consistently tracked across data sources with similar codes grouped together to make data analytically ready.
Enabling the digital health ecosystem
Analytically-ready data enables interoperability solutions, digital clinical trials, and extensive other use cases.
Build your own solutions via a set of robust self-service APIs. Or take advantage of the cloud-native, full-stack Orchestrate platform.
From ingestion and aggregation of raw data, to standardization and harmonization of disparate data types